Abstract: Abstract: Test models are required to evaluate
and benchmark algorithms and tools which support model
driven development. In many cases, test models are not
readily available from real projects and they must be
generated. Using existing model generators leads to test
models of poor quality because they randomly apply graph
operations on graph representations of models. Some
approaches do not even guarantee the basic syntactic
correctness of the created models. This paper presents the
SiDiff Model Generator, which can generate models and
model histories and which can modify existing models. The
resulting models are syntactically correct, contain
complex structures, and have specified statistical
properties, e.g. the frequencies of model element
types.